DialoKG: Knowledge-Structure Aware Task-Oriented Dialogue Generation

This repository contains PyTorch code for NAACL 2022 paper: DialoKG: Knowledge-Structure Aware Task-Oriented Dialogue Generation [PDF].

⚙️ Installation (anaconda)

conda create -n dialokg -y python=3.7 && source activate dialokg
pip install -r requirements.txt
chmod +x setup.sh
./setup.sh

🏋️ Training

# training using a single gpu
python train.py --dataset <DATASET-NAME> --params_file config/gpt2/params.json --device cuda

# training in a distributed setting
python -m torch.distributed.launch --dataset <DATASET-NAME> --params_file config/gpt2/params.json --device cuda

Valid dataset names: incar, camrest, woz2.1 .

🎯 Evaluation

python eval.py --generate runs/dialokg-gpt2/<DATASET-NAME> --dataset <DATASET-NAME>  --generation_params_file config/gpt2/generation_params.json --eval_dataset test  --output_file outputs/<DATASET-NAME>-output.json --top_weights 7

Note that:

• the best parameters are reported in the paper.
• reported results are obtained from distributed training.

📝 Citation

@inproceedings{rony-etal-2022-dialokg,
    title = "{D}ialo{KG}: Knowledge-Structure Aware Task-Oriented Dialogue Generation",
    author = "Rony, Md Rashad Al Hasan  and
      Usbeck, Ricardo  and
      Lehmann, Jens",
    booktitle = "Findings of the Association for Computational Linguistics: NAACL 2022",
    month = jul,
    year = "2022",
    address = "Seattle, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2022.findings-naacl.195",
    pages = "2557--2571",
    abstract = "Task-oriented dialogue generation is challenging since the underlying knowledge is often dynamic and effectively incorporating knowledge into the learning process is hard. It is particularly challenging to generate both human-like and informative responses in this setting. Recent research primarily focused on various knowledge distillation methods where the underlying relationship between the facts in a knowledge base is not effectively captured. In this paper, we go one step further and demonstrate how the structural information of a knowledge graph can improve the system{'}s inference capabilities. Specifically, we propose DialoKG, a novel task-oriented dialogue system that effectively incorporates knowledge into a language model. Our proposed system views relational knowledge as a knowledge graph and introduces (1) a structure-aware knowledge embedding technique, and (2) a knowledge graph-weighted attention masking strategy to facilitate the system selecting relevant information during the dialogue generation. An empirical evaluation demonstrates the effectiveness of DialoKG over state-of-the-art methods on several standard benchmark datasets.",
}

📜 License

MIT

📪 Contact

For further information, contact the corresponding author Md Rashad Al Hasan Rony (email).